Autonomous Adaptive Sensing for Energy-Efficient IoT Applications. Frank Alexander Kraemer

Transcription

1 Autonomous Adaptive Sensing for Energy-Efficient IoT Applications Frank Alexander Kraemer

2 Internet of Things Challenges: The scale in terms of the number of units. The constraints in terms of resources: energy, memory, computation. The nonstationary and heterogeneous environments of things.

3 COPD Patient Monitoring [46] Parkinson Speech Analysis [47] Vital Signs Monitoring [49] Real-time Epileptic Seizure Detection [50] Ubiquitous Emergency [51] Cloud Backend- server Cloud server Cloud center WAN Gateway Gateway Emergency room LAN PAN / BAN WLAN / GPRS MBU Bluetooth Sensors Intel edison Android tablet Bluetooth 4.0 Smart- watch Management subsystem Snmart shirt DP WTB DAPB Sensors BP MDC Mobile device Bluetooth 4.0 EEG - sensors n e911 system Computer in ambulance Handheld device Medical device Patients' smartphone Mobile Home Hospital Non-hospital Transport Kraemer, F. A., Braten, A. E., Tamkittikhun, N., & Palma, D. (2017). Fog Computing in Healthcare A Review and Discussion. IEEE Access,

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6 Internet of Things Challenges: The scale in terms of the number of units. The constraints in terms of resources: energy, memory, computation. The nonstationary and heterogeneous environments of things. Opportunities: Many applications only need to provide an overall picture of a situation. Nodes can do energy harvesting. The cloud can support nodes with computation. There is much data to learn from.

7 IKTPLUSS: ART Autonomous Resource-Constrained Things Phase 1 March to December NOK Academic Workshop Postdoc in Statistics Search for Industry Partners Phase 2 ca. 4 years 4 M NOK / year IES + IIK + IMF PhD in Statistical Learning for Autonomous Resource-Constrained Sensors NTNU PhD position on enabling technologies

8 IoT - A Discipline? Dependability Security IoT Embedded Systems Networking AI Statistics Signal Processing

9 IoT AI 1. Use IoT infrastructure to deliver data into AI processes 2. Use AI to optimize the IoT infrastructure. 3. Use IoT to execute and distribute AI

10 IoT - A Discipline? Dependability Security IoT Embedded Systems Networking AI Statistics Signal Processing

11 COPD Patient Monitoring [46] Parkinson Speech Analysis [47] Vital Signs Monitoring [49] Real-time Epileptic Seizure Detection [50] Ubiquitous Emergency [51] Cloud Backend- server Cloud server Cloud center WAN Gateway Gateway Emergency room LAN PAN / BAN WLAN / GPRS MBU Bluetooth Sensors Intel edison Android tablet Bluetooth 4.0 Smart- watch Management subsystem Snmart shirt DP WTB DAPB Sensors BP MDC Mobile device Bluetooth 4.0 EEG - sensors n e911 system Computer in ambulance Handheld device Medical device Patients' smartphone Mobile Home Hospital Non-hospital Transport

12 Strategies Adaptive sensing: Only sense what is interesting Autonomous sensing: Without human intervention

13 Dependability vs. Constraints Dependable: Do not introduce single-point-of-failures, operate in isolation Constrained: Work on constrained nodes Independent operation, learning, executing. Dependability: +++ Energy: --- Stupid endpoint, depends on cloud Dependability: --- Energy: +++

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16 Should you compute locally? How much energy should I use? How much energy do I have? How much energy will I harvest? Which radio should I use? When will connectivity be great again? How should I send my data? Energy Network Sensing Security How often should I measure? How many measurements should I take? How valuable is my next sample? In which environment am I? Can I trust my peers? Is this a normal situation? Should I store this? What should I send first? How much storage for what? Storage Context Where am I? What do I need to know about my environment? When will my environment change?

17 Autonomous Sensor Test Bed

18 Autonomous Adaptive Sensing Let sensors benefit from big data and machine learning Let sensors learn from each other All parameters in sensing applications are subject to learning Learning = autonomous optimization

19 April 7th April 9th April 12th :00 6:00 9:00 12:00 15:00 18:00 21:00

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22 Server Backend Weather Forecast LoRaWAN Gateway TTN Upstream: sensor data battery voltage solar current sensing mode Downstream: sensing mode update 1 2 8

23 TTN (1).csv Filtered sensor data Prepare training samples (3) Learning solar energy intake (4) Predict solar energy intake (5) Predicted solar energy Plan met.no (2).csv Filtered weather data Learning battery gain Learning energy consumption Predict battery gain (6) Predict energy consumption (7) Planning (8) Sensing mode update Predicted battery level (9) Predicted energy consumption Predicted battery level Collect Learn Predict Plan

24 Energy Consumption Estimation Energy Planning Temp. Mode T1 Temp. Mode T2 Data Analysis Device Management Sleep 24% Sense 67% Send 9% Process ~0% Sleep 14% Sense 81% Send 5% Process ~0% LoRaWAN Gateway Upstream: sensor data energy consumption battery level Particle Mode P1+P2 Sleep 39% Sense 61% Send ~0% Process ~0% GPIO Sleep 52% Sense 8% Send 40% Process ~0% Downstream: sensing mode Sensor Nodes Tamkittikhun, N., Hussain, A., Kraemer, F. A. Energy Consumption Estimation for Energy-Aware, Adaptive Sensing Applications. International Conference on Mobile, Secure and Programmable Networking (MSPN'2017)

25 Energy Consumption Estimation

26 Energy Consumption Estimation Power send sense sense compute pre-sleep sleep sleep t post-sleep wait pre-send post-send